RNN architecture is preferred for sequential data. The most widely used RNN architecture is LSTMs. LSTM consists of memory blocks and memory cells, along with gate units [1]}. They are designed in such a way that they can remember values at arbitrary intervals. The proposed architecture of the LSTM based DAE model is described in figure 6. The LSTM performs the encoding and decoding of LSTM based DAE. Two LSTM layers are used for encoding, and a repeat vector and an LSTM layer is used in decoding, as shown in figure 6. Here, the LSTM encoder extracts the ECG signals' features while the decoder converts the feature maps to the output. Parameters of the encoders and decoders are computed by using an unsupervised training process.
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